Modelling the impacts of pests and diseases on agricultural systems

Abstract

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

Keywords: Model coupling; Model integration; Modelling frameworks; Process-based models; Yield loss.

Fig. 1

Prediction and empiricism levels in process-based crop simulation models. Redrawn from Acock and Acock (1991).

Fig. 2

A summary flowchart of steps involved in the modelling of crop – pathogen and pest systems.

Fig. 3

Schematic representation of the four Diseases components (coloured boxes) and of their interaction (grey arrows). For each component, the main processes, inputs and outputs are reported, with charts presenting sample simulations. The variables shared among Diseases components are reported in italics; the variables produced by the crop model are reported in bold. HT = host tissue, AGB = aboveground biomass, LAI = leaf area index.

Fig. 4

A roadmap for pest-disease-crop integrated model development